Abstract
Pretrained language models (PLMs) have achieved superhuman performance on many benchmarks, creating a need for harder tasks. We introduce CoDA21 (Context Definition Alignment), a challenging benchmark that measures natural language understanding (NLU) capabilities of PLMs: Given a definition and a context each for k words, but not the words themselves, the task is to align the k definitions with the k contexts. CoDA21 requires a deep understanding of contexts and definitions, including complex inference and world knowledge. We find that there is a large gap between human and PLM performance, suggesting that CoDA21 measures an aspect of NLU that is not sufficiently covered in existing benchmarks.- Anthology ID:
- 2022.acl-short.92
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 815–824
- Language:
- URL:
- https://aclanthology.org/2022.acl-short.92
- DOI:
- 10.18653/v1/2022.acl-short.92
- Cite (ACL):
- Lütfi Kerem Senel, Timo Schick, and Hinrich Schuetze. 2022. CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 815–824, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment (Senel et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.acl-short.92.pdf
- Code
- lksenel/coda21